

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      Dict   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__  ,	a__=False  ,	a__=False):

			'''simple docstring'''



			a_						:						Tuple 							= """backbone.""" if is_semantic else """"""

			a_						:						List[str] 							= []
			for i in range(config.num_hidden_layers):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight'''))
						rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias'''))
						rename_keys.append(
						    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight'''))
						rename_keys.append(
						    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias'''))
						rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight'''))
						rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias'''))
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight'''))
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias'''))
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight'''))
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias'''))

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        (f'''{prefix}cls_token''', """beit.embeddings.cls_token"""),
			        (f'''{prefix}patch_embed.proj.weight''', """beit.embeddings.patch_embeddings.projection.weight"""),
			        (f'''{prefix}patch_embed.proj.bias''', """beit.embeddings.patch_embeddings.projection.bias"""),
			        (f'''{prefix}pos_embed''', """beit.embeddings.position_embeddings"""),
			    ])

			if has_lm_head:
						# mask token + layernorm
						rename_keys.extend(
						    [
						        ("""mask_token""", """beit.embeddings.mask_token"""),
						        ("""norm.weight""", """layernorm.weight"""),
						        ("""norm.bias""", """layernorm.bias"""),
						    ])
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
						        ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
						        ("""head.weight""", """classifier.weight"""),
						        ("""head.bias""", """classifier.bias"""),
						    ])

			return rename_keys


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__=False  ,	a__=False):

			'''simple docstring'''



			for i in range(config.num_hidden_layers):
						a_						:						Any 							= """backbone.""" if is_semantic else """"""
						# queries, keys and values
						a_						:						Tuple 							= state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight''')
						a_						:						Union[str, Any] 							= state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias''')
						a_						:						Union[str, Any] 							= state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias''')

						a_						:						Optional[int] 							= in_proj_weight[
						    : config.hidden_size, :
						]
						a_						:						Dict 							= q_bias
						a_						:						List[str] 							= in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						a_						:						Optional[Any] 							= in_proj_weight[
						    -config.hidden_size :, :
						]
						a_						:						List[Any] 							= v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						a_						:						Dict 							= state_dict.pop(f'''{prefix}blocks.{i}.gamma_1''')
						a_						:						Union[str, Any] 							= state_dict.pop(f'''{prefix}blocks.{i}.gamma_2''')

						a_						:						int 							= gamma_a
						a_						:						Dict 							= gamma_a


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						int 							= dct.pop(a__)
			a_						:						Tuple 							= val


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						Union[str, Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__=False):

			'''simple docstring'''



			a_						:						List[Any] 							= False if """rvlcdip""" in checkpoint_url else True
			a_						:						Optional[int] 							= BeitConfig(use_absolute_position_embeddings=a__  ,	use_mask_token=a__)

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						a_						:						int 							= 1_0_2_4
						a_						:						Any 							= 4_0_9_6
						a_						:						str 							= 2_4
						a_						:						List[Any] 							= 1_6

			# labels
			if "rvlcdip" in checkpoint_url:
						a_						:						Optional[int] 							= 1_6
						a_						:						str 							= """huggingface/label-files"""
						a_						:						int 							= """rvlcdip-id2label.json"""
						a_						:						Any 							= json.load(open(hf_hub_download(a__  ,	a__  ,	repo_type="""dataset""")  ,	"""r"""))
						a_						:						Optional[int] 							= {int(a__): v for k, v in idalabel.items()}
						a_						:						Any 							= idalabel
						a_						:						Dict 							= {v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			a_						:						Union[str, Any] 							= torch.hub.load_state_dict_from_url(a__  ,	map_location="""cpu""")["""model"""]

			a_						:						Dict 							= create_rename_keys(a__  ,	has_lm_head=a__)
			for src, dest in rename_keys:
						rename_key(a__  ,	a__  ,	a__)
			read_in_q_k_v(a__  ,	a__  ,	has_lm_head=a__)

			# load HuggingFace model
			a_						:						Any 							= BeitForMaskedImageModeling(a__) if has_lm_head else BeitForImageClassification(a__)
			model.eval()
			model.load_state_dict(a__)

			# Check outputs on an image
			a_						:						Union[str, Any] 							= BeitImageProcessor(
			    size=config.image_size  ,	resample=PILImageResampling.BILINEAR  ,	do_center_crop=a__)
			a_						:						str 							= prepare_img()

			a_						:						List[str] 							= image_processor(images=a__  ,	return_tensors="""pt""")
			a_						:						List[str] 							= encoding["""pixel_values"""]

			a_						:						List[Any] 							= model(a__)
			a_						:						str 							= outputs.logits

			# verify logits
			a_						:						List[str] 							= [1, 1_6] if """rvlcdip""" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
			assert logits.shape == torch.Size(a__), "Shape of logits not as expected"

			Path(a__).mkdir(exist_ok=a__)
			print(f'''Saving model to {pytorch_dump_folder_path}''')
			model.save_pretrained(a__)
			print(f'''Saving image processor to {pytorch_dump_folder_path}''')
			image_processor.save_pretrained(a__)

			if push_to_hub:
						if has_lm_head:
									a_						:						Optional[Any] 							= """dit-base""" if """base""" in checkpoint_url else """dit-large"""
						else:
									a_						:						Tuple 							= """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
						image_processor.push_to_hub(
						    repo_path_or_name=Path(a__  ,	a__)  ,	organization="""nielsr"""  ,	commit_message="""Add image processor"""  ,	use_temp_dir=a__  ,	)
						model.push_to_hub(
						    repo_path_or_name=Path(a__  ,	a__)  ,	organization="""nielsr"""  ,	commit_message="""Add model"""  ,	use_temp_dir=a__  ,	)


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
	    type=str,
	    help="""URL to the original PyTorch checkpoint (.pth file).""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	__snake_case				:      int   		=			parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case				:      Optional[int]   		=			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMForMultipleChoice""",
	    """XLMForQuestionAnswering""",
	    """XLMForQuestionAnsweringSimple""",
	    """XLMForSequenceClassification""",
	    """XLMForTokenClassification""",
	    """XLMModel""",
	    """XLMPreTrainedModel""",
	    """XLMWithLMHeadModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMForMultipleChoice""",
	    """TFXLMForQuestionAnsweringSimple""",
	    """TFXLMForSequenceClassification""",
	    """TFXLMForTokenClassification""",
	    """TFXLMMainLayer""",
	    """TFXLMModel""",
	    """TFXLMPreTrainedModel""",
	    """TFXLMWithLMHeadModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	__snake_case				:      Union[str, Any]   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


